The disclosed computer-implemented method may include matching transportation requests to personal mobility vehicles. A dynamic transportation network may incorporate different types of vehicles, such as bicycles and/or scooters. Certain vehicles may have advantages over other vehicles in certain contexts but be disadvantageous in others. For example, a dynamic transportation matching system may match a user transporting a bulky package with a basket-equipped bike rather than a scooter without a basket. Moreover, the dynamic transportation matching system may account for a wide variety of other factors, including but not limited to route features, ambient conditions, and vehicle status when matching a transportation requestor to a specific vehicle. Moreover, some systems may account for vehicle wear-and-tear, battery power levels, operational status, etc. to avoid matching users vehicles that would be unable to fulfill a transportation request. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for estimating battery-powered driving distance for a vehicle, including training a relative model for a battery using input historical battery temperature data and historical battery-external factors, and predicting a future battery temperature based on the relative model and one or more of current or future battery-external factors. A battery power capacity is determined using the predicted future battery temperature and input manufacturer specifications for the battery, and a remaining battery powered driving distance is calculated based on input vehicle power consumption data and the determined battery power capacity.

An accurate and reliable maintenance method for controlling a condition of a wear and tear element of a track-bound vehicle includes determining a second condition of the element, being a chronological subsequent condition to a first condition, by starting from the first condition of the element, by using a machine learning algorithm, representing a chronological behavior of the element. A first action performable on the element is determined by using the determined second condition of the element and at least one predefined conditional criterion for the element. A resulting third condition of the element is determined by using a conditional change of the element. The conditional change is a consequence of the first performable action. A second performable action performable on the element is determined one more time by using the resulting third condition of the element and at least one predefined conditional criterion for the element.

A charging facility guidance system is equipped with an electric vehicle and a server device. The electric vehicle is equipped with a first processor transmitting information on a current position and a remaining battery level of the electric vehicle to the server device. The server device is equipped with a second processor calculating a possible cruising distance of the electric vehicle based on the remaining battery level thereof, setting a charging facility search range indicating a range of search for charging facilities for the electric vehicle along a route from a place of departure of the electric vehicle to a destination of the electric vehicle, searching for charging facilities included in the charging facility search range, based on the possible cruising distance, selecting the charging facility located as close as possible to the destination, and transmitting information on the selected charging facility to the electric vehicle.

A travel management platform can determine a refueling event for a vehicle, can identify a plurality of refueling stations based on the refueling event, can provide, to the plurality of refueling stations, one or more parameters for the refueling event, can receive respective offers for the refueling event, can select an offer for the refueling event from the respective offers for the refueling event, and can perform various actions, such as transmitting an instruction to autonomously navigate the vehicle to a refueling station associated with the offer for the refueling event, transmitting an instruction to display a notification associated with the offer for refueling event, transmitting information associated with the offer for refueling event, transmitting information associated with the refueling station associated with the offer for the refueling event, and/or the like.

A fuel system controller obtains fuel burn data from an engine control module (ECM). The fuel system controller also obtains location data from a telematics control module, such as GPS location data identifying the location of a vehicle. The fuel system controller determines the vehicle's base location based on the location data, and determines how far the vehicle can travel based on the fuel burn data. The fuel system controller determines how many fueling stations are with a threshold distance of the determined distance to empty. The fuel system controller can use that data to identify which, and how many, fueling stations are within a threshold distance of the determined distance to empty. The fuel system controller can provide a fueling warning indication based on the number of fueling stations that are within the determined distance to empty.

A depleted EV transmits first information including a current location of the depleted EV to a server. Each of the other vehicles transmits second information including a current location of the vehicle to the server. When the server receives from the depleted EV a help signal requesting power supply from another vehicle to the depleted EV, the server selects, from among the other vehicles, a rescue EV to supply electric power to the depleted EV, using the first information and the second information.

Approaches for predicting parameters contributing to engine emissions are described. In an example, the values of control parameters may be obtained from the vehicle sensors. Based on the obtained values of the control parameters, estimated emission value may be determined pertaining to a correlation criterion reflecting a predetermined relationship between the obtained control parameter and engine emission. Further, the contribution index of each of the individual control parameters may be identified. Further, based on the estimated emission value and the contribution index, aggregated emission value corresponding to the exhausted emission from the engine for particular trip may be calculated.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for unmanned aerial vehicle authorization and geofence envelope determination. One of the methods includes determining, by an electronic system in an Unmanned Aerial Vehicle (UAV), an estimated fuel remaining in the UAV. An estimated fuel consumption of the UAV is determined. Estimated information associated with wind affecting the UAV is determined using information obtained from sensors included in the UAV. Estimated flights times remaining for a current path, and one or more alternative flight paths, are determined using the determined estimated fuel remaining, determined estimated fuel consumption, determined information associated wind, and information describing each flight path. In response to the electronic system determining that the estimated fuel remaining, after completion of the current flight path, would be below a first threshold, an alternative flight path is selected.

The present disclosure relates to systems and methods for forecasting power usage of an aerial vehicle. An illustrative system includes an aerial vehicle including at least one component, and a computing device communicatively coupled to the aerial vehicle. The computing device includes a processor and a memory storing instructions which, when executed by the processor, cause the computing device to receive power consumption data corresponding to the at least one component, and generate a simulation model of power usage based on the power consumption data corresponding to the at least one component.